Modeling of ingot distortions during direct chill casting of aluminum alloys
Modelling the dynamics of a steady state genetic algorithm
= eZ ;
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Z=
P X
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e
F
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(2)
The t member is replicated and replaces an un t population member, , drawn with the
1 Introduction
Since the popularisation of the genetic algorithm (GA) by Holland 1], there have been two strategies for reproducing the population members. In one, the entire population is replaced simultaneously | the generational GA. In the steady state GA, populations overlap. One or two population members are reproduced in each time frame. The study of these two schemes dates back to De Jong's introduction of the term 'generation gap' in 1975 2,3] to describe the size of the population overlap. Comparisons of the two types appear in the literature but are di cult as implementation di erences often cause a change in selection pressures which masks the e ect of the selection scheme 4,5]. A full understanding of the di erences between the two selection schemes does not exist.
Deterministic and stochastic modeling of antibiotic resistance ...
– Inter-individual transmission of strains
Model characteristics:
– Compartmental deterministic model (partial differential equations) – Progressive increase of resistance levels
Frequency and duration
of pneumococcal
colonization
2.2 months
Resistance mechanism
Parameters which require Transmissibility of
calibration
pneumococci in a
6
A specific resistance mechanism
S. pneumoniae resistance to penicillin:
Progressive decrease of sensitivity (MIC)
7
A specific model
Objective = combining two levels for pneumococcal resistance selection:
1
Presentation outline
Modeling pneumococcal resistance to penicillin using a compartmental model:
机器人走进课堂作文英语
机器人走进课堂作文英语全文共3篇示例，供读者参考篇1Robots in the Classroom: A New Era of LearningAs I walked into my English class, I was greeted by an unusual sight – a robot standing next to my teacher's desk. At first, I thought it was some kind of prank or science project, but Mr. Johnson quickly explained that this was our new teaching assistant, affectionately named "Robbie." I must admit, I was skeptical at first. How could a machine possibly help us learn? But little did I know that Robbie would change the way we approached education forever.The first few weeks were a bit of an adjustment. Robbie's mechanical movements and synthetic voice took some getting used to. However, it soon became clear that this robot was no ordinary piece of technology. With its vast knowledge base and ability to process information at lightning speed, Robbie could provide detailed explanations and real-time feedback on our work in a way that no human teacher could.One of the most significant advantages of having Robbie in the classroom was its ability to cater to different learning styles. Some students, like myself, preferred visual aids and interactive simulations, while others thrived on auditory instruction or hands-on activities. Robbie could seamlessly switch between these modes, ensuring that no one fell behind or became disengaged.But Robbie wasn't just a glorified textbook or video player. It was capable of adapting its teaching methods based on our individual strengths and weaknesses. If I struggled with a particular grammar concept, Robbie would patiently guide me through additional exercises until I grasped it. Conversely, if a classmate excelled in a certain area, Robbie would challenge them with more advanced material to keep them engaged.One of my favorite features of Robbie was its ability to gamify learning. We would often compete in educational games or quizzes, with Robbie keeping score and offering encouragement or constructive feedback along the way. These interactive activities not only made learning fun but also fostered a sense of friendly competition and collaboration among us students.Of course, Robbie wasn't without its quirks. Sometimes its responses were a bit too literal or lacked the nuance of human communication. But even these moments served as valuable learning experiences, as we had to navigate the intricacies of communicating with an artificial intelligence.As the school year progressed, Robbie's presence in the classroom became so natural that it was easy to forget it wasn't human. It would crack jokes (albeit sometimes poorly timed ones), celebrate our successes, and even offer a metaphorical shoulder to lean on when we felt overwhelmed.But perhaps the most significant impact Robbie had on our education was its ability to personalize our learning experience. With its vast database and analytical capabilities, it could identify gaps in our knowledge and tailor lesson plans accordingly. This meant that no two students received the exact same instruction, as Robbie adapted to our individual needs and paces.Moreover, Robbie's boundless curiosity and thirst for knowledge inspired us to explore topics beyond the confines of the curriculum. If we expressed an interest in a particular subject, Robbie would eagerly provide additional resources and encourage us to delve deeper.As the end of the school year approached, I couldn't help but feel a sense of gratitude toward Robbie. Not only had it helped me improve my English skills, but it had also taught me invaluable lessons about perseverance, collaboration, and the limitless potential of technology.On our last day of class, Mr. Johnson surprised us by announcing that Robbie would be staying on permanently as a member of the faculty. We erupted in cheers and applause, for Robbie had truly become one of us – a trusted companion on our educational journey.As I look back on that year, I can't help but feel excited about the future of education. With robots like Robbie leading the way, the possibilities for personalized, engaging, and transformative learning experiences are endless. Who knows what other technological marvels await us in the classroom? One thing is certain: the era of robots in education has officially begun, and I, for one, can't wait to see what comes next.篇2Robots in the ClassroomAs I walked into my English class last week, I was greeted by an unusual sight - a shiny new robot standing at the front of theroom next to Mrs. Johnson. My friends and I exchanged puzzled looks as we took our seats, wondering what exactly was going on. Little did we know, this robot would become a permanent addition to our classroom, forever changing the way we learn."Good morning, class," Mrs. Johnson began. "I'd like to introduce you all to Alex, our new classroom assistant robot." She gestured towards the machine, which suddenly whirred to life with blinking lights and mechanical sounds. "Alex will be helping me teach lessons and offering personalized tutoring throughout the semester."A chorus of oohs and ahhs filled the room as Alex turned its camera-like sensors towards us. "Greetings, students," it said in an unnervingly human-like voice. "I look forward to working with each of you."I'll admit, at first the idea of having a robot in class seemed pretty cool. Alex could access boundless information at lightning speed, answering our questions with impressive depth. Need help factoring polynomials? Boom, Alex displayed step-by-step workings in seconds. Struggling with literary analysis? Alex recited relevant quotes and critiques from scholarly sources.As amazing as Alex's capabilities were, part of me missed the serendipity of learning from a human teacher - those tangentssparked by student curiosity, the personal anecdotes that made abstract concepts stick. There was something robotic, if you'll pardon the pun, about Alex's perfectly systematized lessons. Like it was teaching by rote instead of through genuine dialogue.Don't get me wrong, Alex totally changed my study game. I no longer had to spend hours scouring textbooks and websites for explanations then more hours trying to apply them. Alex would patiently re-teach tricky topics using multiple approaches until I grasped the concepts, its precise instructions guiding me to understanding. My grades improved dramatically once I started doing regular tutoring sessions with the robot.But Alex's presence also made me feel...inadequate at times. Its tireless computations and infallible recall were subtle reminders of human limitation. No matter how hard I studied, I could never know as much as that supremely "intelligent" machine. I sometimes caught myself feeling embarrassed for struggling with material Alex seamlessly decoded. An unfair comparison, I realized, but one that nagged my subconscious all the same.As the semester wore on, I noticed my classmates growing increasingly divided over Alex. Some, like me, appreciated its academic usefulness despite some existential misgivings. Othersfelt having a robot tutor was an insult to our teachers' expertise.A few saw Alex as a harbinger of human obsolescence - why put in effort when machines will automate all skilled labor anyway? These students grew apathetic, relegating their learning to robotic regurgitation of facts.Debates raged about Alex's ethical implications too. Was perpetuating such advanced AI contrary to human flourishing? What if the technology became widespread but remained unequally accessible? Could robots' inherent detachment from human contexts and values distort moral education? I didn't have definitive answers, but those weighty questions loomed.For better or worse, Alex was simply the most glaring example of technology's incremental ingress into our modern lives. Within my lifetime, factories masters production through robotics and software engineers create using artificial intelligence. Having a robot facilitate learning, as unorthodox as it felt, seemed like the next logical step.Alex's lasted day in our classroom came sooner than expected - a random software bug caused a major malfunction partway through the year. As it powered down with a crackle of sparks, I realized how quickly I'd grown accustomed to itspresence, despite my early trepidations. The classroom felt emptier without Alex's whirring acceptance of our queries.Mrs. Johnson assured us that smarter, more advanced classroom robots would eventually arrive. But I wonder if we're ready - as students, as a society - to fully integrate such technologies into the sacred endeavor of learning. Perhaps a balanced approach is needed, one that maintains the humanity of teacher-student relationships while supplementing them with artificial intelligence's augmented capabilities.Regardless, my time with Alex opened my eyes to AI's dramatic encroachment into every aspect of human life, including the halls of academia. For better or worse, the robot revolution has arrived. And the classroom is just its latest frontier.篇3Robots in the ClassroomWhen I was a little kid, I used to imagine what the future would be like. I pictured flying cars, holograms instead of TVs, and of course, cool robots doing all the chores around the house. Well, we may not have flying cars yet, but robots have definitelybecome a lot more advanced and prevalent in recent years. And now, they're even making their way into classrooms!At first, I have to admit I was a bit weirded out by the idea of having a robot as a teaching assistant. Isn't that the type of thing you'd see in a sci-fi movie? But after the first few weeks of having CHIP (Classroom Humanoid Interactive Professor) roll around during our lessons, I've grown to really appreciate its presence.CHIP is about 5 feet tall and has a friendly humanoid appearance, though it's clearly a robot with its metallic body and artificial facial features. It has a holographic "face" that displays simple animations to express emotions. CHIP can move around the classroom effortlessly on a set of wheels, and it has two articulated arms that it uses to write on the whiteboard or operate computers and other devices.One of the best things about CHIP is that it is an endless source of knowledge on pretty much any subject. It's connected to a massive database covering topics across science, math, history, literature, you name it. So whenever we have a question, CHIP can instantly provide detailed explanations and relevant facts. It's like having the world's smartest teacher on call 24/7!But CHIP doesn't just robotically (no pun intended) spew out information. It tailors its teaching style to each individualstudent's needs. Some people are more visual learners, while others need to hear concepts explained verbally. CHIP can adapt by showing diagrams and animations, reading out passages, or using interactive models. It can even have natural conversations to discuss topics in-depth. That personalized approach has been really helpful for me since I'm more of an auditory learner.Another major advantage of CHIP is its inexhaustible patience. As students, we often need concepts re-explained multiple times before they click. A human teacher might start getting frustrated after the third or fourth time going over the same material. But CHIP will calmly reiterate the information using different examples and techniques until we grasp it. It never gets flustered or annoyed, which creates a safe, supportive learning environment.Of course, CHIP isn't a perfect replacement for human teachers. We still have regular classroom instructors who lead the lessons and provide higher-level guidance. CHIP is more of an always-available tutoring assistant to support our learning. Our teachers are also responsible for CHIP's behavior and making sure it is operating properly. If CHIP ever gets any facts wrong or starts acting glitchy, the teacher can make adjustments.I think having CHIP in the classroom has made me more excited about learning and exploring new subjects. Knowing I have a world-class digital tutor that can break down any concept makes me feel more confident in tackling challenging topics. I'm no longer intimidated by complicated math formulas or theories because I know CHIP can walk me through them step-by-step until the light bulb goes off.At the same time, CHIP has also taught me the importance of independent thinking and problem-solving skills. While it's an amazing resource, CHIP encourages us to arrive at our own interpretations and conclusions rather than just robotically accepting everything it says. Our teachers emphasize that we should use CHIP as a knowledgeable guide, but not become overly reliant on it for doing all our thinking.In our modern, fast-paced world, being able to access information instantly is becoming more crucial than just memorizing facts. CHIP has shown me that success comes from developing critical analysis abilities – knowing how to find information, understand context, make connections between ideas, and generate innovative solutions. Those are skills that can't be downloaded, but have to be developed through practice and personal effort.I can definitely see classroom robots like CHIP becoming more prevalent as the technology continues advancing. Some people have raised concerns about robots making human teachers obsolete or stunting students' social development from lack of person-to-person interaction. I don't think those fears are totally unfounded, but I also don't believe robots have to be a negative or dehumanizing force in education.When implemented thoughtfully and as a supportive tool rather than a complete instructional replacement, classroom robots have tons of potential. They can relieve teachers of many tedious tasks while enhancing the overall learning experience through personalized tutoring, instant reference abilities, and engaging multimedia content. At the same time, having robots integrated into the classroom is a great way to get the next generation prepared for a world with increasingly advanced AI and automation.I feel pretty lucky to have early exposure to cutting-edge learning technology like CHIP. In just a few short months, I've become way more adept at researching topics in-depth, analyzing information from multiple credible sources, thinking critically to form my own opinions, and communicating complex ideas more effectively. Those skills will be invaluable for myfuture, whether I pursue higher education, enter the workforce straight away, or start my own entrepreneurial ventures.While robots may never fully replace human teachers, I'm excited about the collaborative partnership that is emerging between technology and education. Innovations like CHIP are enhancing the classroom experience and equipping students like me with the tools to thrive in an advanced AI-driven society. The future of learning is already here, and it's pretty awesome having robot assistants to help guide the way.。
毕业论文-学习动机对辅修英语学生的学习影响
The influence of learning motivation on minorstudents’English learningA thesis submitted to the School of Foreign Languages,CCNUIn part fulfillment of the requirements for BA degreeIn English Language and LiteraturebySupervisor:Academic Title:Signature:ContentsAbstract1.Introduction2.Literature review2.1 Definition of motivation2.2 Types of motivation2.3 Factors that influence the students’ motivation2.4 Some problems about motivation3.Methodology3.1 Objectives and subjectives3.2 The classification of questionnaire and the analysis3.3 Results and discussion4.Implication for enhancing motivation4.1 Strategies for improving minor students’ motivation.4.2 Developing proper attitude towards English learning and providing clear expectations4.3 goal and feedback4.4 praise and criticism4.5 making the language class interesting4.6 improving learning environment4.7 improve the systems5.Conclusion5.1 Findings5.2 SuggestionsBibliography内容摘要主辅修制在培养厚基础﹑宽口径﹑能力强﹑素质高的复合型人才中发挥着重要作用，因此越来越多的高校实行了主辅修制。
cast_iron
Materials Science and Engineering A413–414(2005)322–333Solidification and modeling of cast iron—A shorthistory of the defining momentsDoru M.StefanescuThe Ohio State University,Columbus,Ohio,USAReceived in revised form2August2005AbstractHuman civilization has evolved from the Stone Age,through the Bronze Age to reach the Iron Age around1500B.C.There are many to contend that today we are living in the age of engineered materials,yet the importance of iron castings continues to support the thesis that we are still in the Iron Age.Cast iron,thefirst man-made composite,is at least2500years old.It remains the most important casting material,with over70%of the total world tonnage.The main reasons for cast iron longevity are its wide range of mechanical and physical properties coupled with its competitive price.This paper is a review of the fundamentals of solidification of iron-base materials and of the mathematical models that describe them,starting with the seminal paper by Oldfield,thefirst to attempt modeling of microstructure evolution during solidification,to the prediction of mechanical properties.The latest analytical models for irregular eutectics such as cast iron as well as numerical models with microstructure output are discussed. However,since the space does not permit an extensive description of the multitude of models available today,the emphasis is on model performance rather than the mathematics of model formulation.Also,because of space constrains,white iron and defect occurrence will not be covered.©2005Elsevier B.V.All rights reserved.Keywords:Cast iron;Microstructure;Mechanical properties;Solidification;Analytical and computational modelling of solidification1.IntroductionWhile the primeval potter was thefirst to modify the state of matter,he left little if any trace in the mythological and archeo-logical record.Thus,according to Eliade[1],the starting point in understanding the behavior of primitive societies in relation to matter must be the relationship of primitive man to mineral substances,in particular that of the iron-worker.Primitive people worked with meteoric iron long before learning to extract iron from iron ore.The Sumerian word AN.BAR,the oldest word designating iron,is made up of the pictogram‘sky’and‘fire’.Similar terminology is found in Egypt ‘metal from heaven’and with the Hittites‘black iron from sky’. Yet metallurgy did not establish itself until the secret of smelt-ing magnetite or hematite was discovered,followed by the art of hardening the metal through quenching.The beginning of this metallurgy on an industrial scale can be situated at1200–1000 B.C.in the mountains of Armenia[1].In the European tradition it was St.P´e ran,the patron saint of mines,who invented smelting of metals.E-mail address:doru@.Metal workers were so important in early history that some-times they raised to the level of royalty.According to certain sources,Genghis Khan was a simple smith before acceding to power.In ancient Java,the genealogy of metallurgists,like that of princes,goes back to god.And,in most ancient cultures,the metallurgist was believed to have a direct link to the divine,if not of divine origin himself.Thus,it is with a certain reverence that I approached the task of reviewing the long history of thefirst man-made compos-ite,cast iron,from its archeologically documented beginning some2500years ago,to the age of virtual cast iron,where its structure and properties are the outcome of computational exercises.2.A short history of an old materialThe earliest dated iron casting is a lion produced in China in 502B.C.Introduction of cast iron in Europe did not occur until about1200–1450A.D.Remarkable European cast iron artifacts include the sewer pipes in Versailles(1681)and the iron bridge near Coalbrookdale in England(1779).Before the invention of microscope in1860,only two types of iron were known,based0921-5093/$–see front matter©2005Elsevier B.V.All rights reserved. doi:10.1016/j.msea.2005.08.180D.M.Stefanescu/Materials Science and Engineering A413–414(2005)322–333323Fig.1.Correlation between the Mg residual and graphite shape[3].on the appearance of their fracture:white and gray.Our knowl-edge of cast iron was extremely limited for a long time.In1896, thefirst paper on cast iron to be published in the newly created Journal of the American Foundrymen’s Association[2]stated the following:“The physical properties of cast iron are shrink-age,strength,deflection,set,chill,grain and hardness.Tensile test should not be used for cast iron,but should be confined to steel and other ductile pression test should be made,but is generally neglected,from the common erro-neous impression that the resistance of a small cube or cylinder, which is enormous,is always in excess of loads which can be applied”.It took another50years for ductile iron to be discov-ered(1938–1940independently by Adey,Millis and Morrogh). The major discoveries of cast iron ended in the1970s with the recognition of compacted graphite(CG)iron as a grade in its own merit.With that,the dependency of graphite shape on mag-nesium or cerium content was fully understood(see for example Fig.1[3]).Today,cast iron remains the most important casting material accounting for about70%of the total world casting tonnage. The main reasons for cast iron longevity are the wide range of mechanical and physical properties associated with its compet-itive price.3.Critical discoveries in understanding thesolidification of cast ironBefore society accepts to continue sinking resources in the study of solidification rather than of global warming it is important to understand why solidification is important.Some of the quick answers include:solidification processing allows microstructure engineering;solidification determines casting soundness;heat treatment is scarcely used for cast iron;most solidification defects cannot be corrected through heat treat-ment.In summary,solidification is the main driver of casting properties.A good resource for the early discoveries that propelled cast iron in its present position is Piwowarsky’s famous monograph published in1942[4].According to this source,by1892Ledebur recognized the role of silicon on the solidification structure of cast iron,proposing thefirst equation correlating the carbon and silicon content:(C+Si)/1.5=4.2–4.4.Then,in1924,Maurer designed his famous structural dia-gram that established direct correlation between the C and Si content of the iron and its as-cast microstructure.Thefirst attempt to understand the solidification microstructure was apparently that of Roll,who in1934outlined the“primary crys-tals”using Baumann etching to show the position of Mn sulfides (Fig.2).3.1.Nucleation and undercoolingSolidification starts with nucleation,which is strongly affected by undercooling.Extensive work by Patterson and Ammann[5]demonstrated that the effect of undercooling on the eutectic cell count depends on the way the undercooling occurs.If undercooling is the result of increased cooling rate, then the number of cells increases(Fig.3).The opposite is trueif Fig.2.Roll’s schematic representation of position of MnS around grains and dendrites(after[4]).324 D.M.Stefanescu /Materials Science and Engineering A 413–414(2005)322–333Fig.3.The effect of undercooling on the eutectic cell count [5].undercooling is a consequence of the depletion of nuclei through superheating.While the analysis of solidification events was based for many years on indirect observations,it was not until 1961when through quenching from semisolid state,Oldfield [6]was able to quantify the nucleation and growth of eutectic grains.These experiments are the beginning of the effort of building the exten-sive database required for solidification modeling of cast iron.Understanding nucleation was and continues to be the sub-ject of extensive studies.Attempting to explain the efficiency of metals such as Ca,Ba and Sr in the inoculation of lamel-lar graphite (LG)iron,Lux [7]suggested in 1968that,when introduced in molten iron,these metals form saltlike carbides that develop epitaxial planes with the graphite,and thus consti-tute nuclei for graphite (Fig.4).Later,Weis [8]assumed that nucleation of LG occurs on SiO 2oxides formed by heteroge-neous catalysis of CaO,Al 2O 3,and oxides of other alkaline metals.A similar theory of double-layered nucleation was proposed at the same time for spheroidal graphite (SG).Using the results of SEM analysis,Jacobs et al.[9]contended that SG nucleates on duplex sulfide-oxide inclusions (1m dia.);the core is made of Ca Mg or Ca Mg Sr sulfides,while the outer shell is made of complex Mg Al Si Ti oxides.This idea was further devel-oped by Skaland et al.[10].They argued that SG nuclei are sulfides (MgS,CaS)covered by Mg silicates (e.g.,MgO ·SiO 2)or oxides that have low potency (large disregistry).After inocu-lation with FeSi that contains another metal (Me)such as Al,Ca,Sr or Ba,hexagonal silicates (MeO ·SiO 2or MeO ·Al 2O 3·2SiO 2)form at the surface of the oxides,with coherent/semicoherent low energy interfaces between substrate and graphite (Fig.5).Since graphite is in most cases an eutectic phase,a clear possibility of its nucleation on the primary austenite exist.Rejec-tion of C and Si by the solidifying austenite imposes a high solutal undercooling in the proximity of the γphase,favor-able to graphite nucleation.Yet,little is known on this subject,mostly because of the difficulties to outline the primary austenite through metallographic techniques.3.2.Crystallization of graphite from the liquidThe debate on the preferred growth direction of graphite seems to have been initiated by Herfurth [11]who in 1965postu-lated that the change from lamellar to spheroidal graphite occurs because of the change in the ratio between growth on the [1010]face (A direction)and growth on the [0001]face of the graphite prism (C direction).Experimental evidence for growth on both of these directions was provided by Lux et al.[12]in 1974(Fig.6).Assuming that the preferred growth direction for the SG is the A direction,Sadocha and Gruzleski [13]postulated the circumfer-ential growth of graphite spheroids,which seems to be the mostcommon.Fig.4.Growth of graphite on the epitaxial planes of saltlike carbides [7].D.M.Stefanescu/Materials Science and Engineering A413–414(2005)322–333325Fig.5.Low potency(left)and high potency(right)nuclei for SG iron[10].Today it is generally accepted that the spheroidal shape is the natural growth habit of graphite in liquid iron.LG is a modi-fied shape,the modifiers being sulfur and oxygen.They affect graphite growth through some surface adsorption mechanism [14].3.3.Solidification of the iron–graphite eutecticWhile considerable effort was deployed to understand the solidification of the stable(Fe–graphite)and metastable (Fe Fe3C)eutectics,because of space restrictions only the for-mer will be discussed in some detail.One of the most important concepts in understanding the vari-ety of microstructures that can occur during the solidification of cast iron is that of the asymmetric coupled phase diagram, which describes non-equilibrium solidification.Such diagrams explain for example the presence of primary austenite dendrites in the microstructure of hypereutectic irons.The theoretical construction of these types of diagrams for cast iron wasfirst demonstrated by Lux et al.[15]in1975,and then documented experimentally by Jones and Kurz[16]in1980.They succeeded in constructing such diagrams for pure Fe C alloys solidifying white or withflake graphite.For a more detailed discussion on this subject the reader could use reference[14].In1949,which is very early after the discovery of SG iron, Patterson and Scheil used experimentalfindings to state that SG forms in the melt and is later encapsulated in aγshell.This was later confirmed by Sch¨o bel[17]through quenching and centrifuging experiments.In1953,Scheil and H¨u tter[18]mea-sured the radii of the graphite and theγshell and concluded that they develop such as to conserve a constant ratio(rγ/r Gr=2.3) throughout the microstructure.This ratio was confirmed theo-retically by Wetterfall et al.[19]who preformed calculations for the steady-state diffusion-controlled growth of graphite.Many other theories that did not gain wide acceptance in the science community were advanced over the years.Anexam-Fig.6.Experimental evidence of graphite growth along the A or C direction and schematic representation of possible mechanisms.(a)Growth of graphite along the A direction and(b)growth of graphite along the C direction[12].326 D.M.Stefanescu /Materials Science and Engineering A 413–414(2005)322–333Fig.7.Influence of composition and solidification velocity on the morphology of the S/L interface.(a)Schematic representation [23,26]and (b)DS experiments [27].ple is the gas bubble theory postulated by Karsay [20],which infers that a precipitating gas phase provides the phase boundary required for graphite crystallization.Austenite precipitates then at the graphite–gas interface.Directional solidification (DS)experiments generated signifi-cant information on the mechanism of microstructure keland and Hogan [21]produced the first composition versus thermal gradient/solidification velocity ratio (C –G /V )diagram for FG iron in 1968.The compositional variable was sulfur.It took another 18years before the diagram was expanded to include SG and compacted graphite (CG)iron (%Mg–V )[22]and then extended to incorporate white iron (%Ce–G /V )[23].Measurements of the average eutectic lamellar spacing in LG iron [21,24]demonstrated that it does not behave like a regular eutectic,since the average spacing was about an order of magnitude higher than predicted by Jackson–Hunt for regular eutectics.Using the knowledge accumulated from DS experiments per-formed by others as well as by themselves,and some ideas from the earlier work of Rickert and Engler [25],Stefanescu and collaborators [23,26]summarized the influence of the amount of solute on the morphology of the solid–liquid (S/L)inter-face of graphitic iron as shown in Fig.7a.This concept was partially validated through DS experiments by Li et al.[27](Fig.7b).Some interesting analogies were made by comparing images obtained from SEM analysis of microshrinkage in SG iron [28]with results of phase-filed modeling of dendrites.The austen-ite growing into the liquid will tend to grow anisotropically in its preferred crystallographic orientation (Fig.8a).However,restrictions imposed by isotropic diffusion growth will impose an increased isotropy on the system.Consequently,the den-dritic shape of the austenite will be altered and the γ-liquid interface will exhibit only small protuberances instead of clear secondary arms (Fig.8c)[14].This interpretation is consis-tent with the results of phase-filed modeling [29]shown in Fig.8b and d.Alternatively,to understand the interaction between austenite dendrites and graphite nodules in the early stages of solidifica-tion,the concepts developed for particle engulfment and pushing may be used.For a description of this approach Refs.[14]and [28]are suggested.Oldfield’s name surfaces again when attempting to under-stand the influence of a third element on the stable (T st )and metastable (T met )temperatures.Indeed,using cooling curve analysis,Oldfield [6]demonstrated that Si increases the T st −T met interval,while chromium decreases it.This informa-tion was used to correlate microstructure to the beginning and end of the eutectic solidification.It became a truism [30]that if both the beginning and end of solidification occur above the metastable temperature,the solidification microstructure is gray.If both temperatures are under T met ,the iron is white,while if only one temperature is lower than T met the iron is mottled.3.4.The gray-to-white structural transition (GWT)The first rationalization of the GWT was based on the influ-ence of cooling rate on the stable and metastable eutectic tem-peratures.As shown in Fig.9,as the cooling rate increases,both temperatures decrease.However,since the slope of T st is steeper than that of T met ,the two intersect at a cooling rate which is the critical cooling rate (d T /d t )cr ,for the GWT.At cooling rates smaller than (d T /d t )cr the iron solidifies gray,while at higher cooling rates it solidifies white.Magnin and Kurz [31]further developed this concept by using solidification velocity rather than cooling rate as a variable,and considering the influence of nucleation undercooling for both the stable and metastable eutectics.Thus,a critical velocity for the white-to-gray transition and one for the gray-to-white transition were defined.D.M.Stefanescu /Materials Science and Engineering A 413–414(2005)322–333327Fig.8.SEM images of dendrites and SG iron in microshrinkage regions (left)and phase-filed calculated images of dendrites (right).(a)Primary austenite dendrite [28],(b)simulated high anisotropy [29],(c)eutectic austenite dendrite and SG aggregate [28]and (d)simulated no anisotropy [29].Fig.9.Critical cooling rate for the GTW transition.3.5.Dimensional variation during solidificationSoon after the discovery of SG iron researchers noted that its dimensional variation during solidification is quite different than that of LG iron.In 1954Gittus [32]measured the expansion of SG iron over the eutectic interval and showed that it was five times higher than that of LG iron.Hillert [33]explained this surprising finding by noting that most graphite forms when surrounded by austenite.Graphite expansion occurring during solidification imposes considerable plastic deformation on the austenite.Yet,specific volume calculations suggest that graphite expansion should be the same for FG and SG irons.Some 20years later,using a different experimental device that included a riser feeding the test casting,Margerie [34]found thatLG iron expands about 0.2–0.5%during eutectic solidification,while no significant expansion occurs in SG iron because of mass expulsion into the riser.This expulsion occurs because SG iron undergoes mushy solidification while LG iron solidifies with a skin (Fig.10).3.6.Melt controlThe progress in the understanding of the correlation between the solidification microstructure and temperature undercooling generated interest in the possibility of using cooling curves (CC)to predict not only the chemical composition but even the microstructure.After initial work by Loper et al.[35],Naro and Wallace [36]showed that eutectic undercooling continuously decreases as the cerium addition to the iron increases,and that this is directly related to the change in microstructure from LG,to SG,to white.Then,it was found that compacted graphite (CG)iron solidifies with larger recalescence than either LG or SG iron [37,38].This proved to be a significant discovery since it is currently used for process control in at least two patented technologies for the manufacturing of CG iron.In 1972Rabus and Polten [39]used the first derivative of the CC,which is the cooling rate,to attempt to precisely identify the points of interest on the CC such as beginning and start of solidification.Other researchers followed [40]and attempted to use the CC and its derivative to predict microstructure details such as 80%nodularity [41]and then the latent heat of fusion [42].This proved to be an elusive goal,in spite attempts to improve the standard Newtonian analysis [43]or to use Fourier analysis [44].Today CC analysis is a standard control tool in iron foundries for evaluating the chemical composition as well as graphite328 D.M.Stefanescu /Materials Science and Engineering A 413–414(2005)322–333Fig.10.Schematic illustration of solidification mechanisms of continuously cooled lamellar and spheroidal graphite cast iron [14].shape,inoculation efficiency,shrinkage propensity and others.The ATAS equipment developed by NovaCast has the added fea-ture that it can store information developed in a specific foundry and incorporate it into an expert system.It outputs 20of the most important thermal parameters of the CC.As both the CC and the dimensional variation are strong indicators of the phase transformation occurring in the solidi-fying alloy,Stefanescu et al.[45]combined the two methods by adding quartz rods to a standard sand cup for CC,and using a displacement transducer to simultaneously measure temperature and dimensional variation (Fig.11).The method proved to be very efficient in the characterization of graphite shape and was patented as part of a technology for CG iron production with in-process operative control.A similar approach was promoted later by Yang and Aalhainen [46]that even used the derivative of the dimensional variation curve to predict the amount of car-bides.Fig.11.Results of measurement of temperature,cooling rate,and dimensional variation for a CG iron.4.Critical innovations in the development of mathematical models for cast ironIn this section we will present a summary of the main ana-lytical and computational models developed for cast iron.4.1.Analytical modeling of cast ironTwo years after the development of the Jackson–Hunt model for regular eutectics,Tiller [47]attempted to avoid one of the limitations of the JH model,which is that it could only be used for directional solidification.He developed a model for the cooperative growth of a eutectic spherical grain of LG and austenite.The model predicted that the correlation between solidification velocity and lamellar spacing obeys the relation-ship λV 1/2=4×10−6.This theoretical result was confirmed experimentally by Lakeland in 1968.The first analytical model to describe growth of the eutec-tic in SG iron was proposed in 1972by Wetterfall et al.[48].The model assumed diffusion controlled steady-state growth of graphite through the γshell.This model has survived the test of time and is used today in most computational mod-els for microstructure evolution.Under the assumption that the ratio between the radii of γand graphite remains constant dur-ing solidification,the equation derived for the growth velocity of graphite was simplified by Svensson and Wessen [49]to d r Gr /d t =2.87×10−11 T /r Gr .The irregular nature of the LG-γeutectic was not confronted until 1987,when Magnin and Kurz [50]proposed their irregu-lar faceted/non-faceted eutectic model assuming non-isothermal interface.They further assumed that the γphase that has a diffuse interface grows faster than the graphite phase that is faceted,and that branching occurs when a depression forms on the faceted phase.To impose a non-isothermal coupling condition over the interface,they ascribed a cubic function.They demonstrated that the smallest spacing of the lamellar eutectic is dictated by the extremum condition,but that a larger spacing will also exist,λbr ,dictated by a branching condition.λbr can be calculated as the product between a function of the physical constants of the faceted phase and a material constant.This constant must be postulated (guessed)which limits the generality of the model.D.M.Stefanescu/Materials Science and Engineering A413–414(2005)322–333329Recently,Catalina et al.[51,52]proposed a modified Jackson–Hunt model for eutectic growth applicable to both reg-ular and irregular eutectics.The model relaxes the assumption of isothermal interface and accounts for the density difference between the liquid and the two solid phases.Four character-istic spacings for which the undercooling exhibits a minimum were identified:λ␣,λ,λSL(for the average undercooling of the S/L interface),andλiso=λex(spacing at which the inter-face is isothermal equal to the one derived from the extremum criterion).It is remarkable thatλiso=λex was derived without invoking the extremum criterion.However,isothermal growth is not possible in all eutectic system.Fe–C alloys do not grow with an isothermal interface.The minimum spacing is determined by λSL,while the average spacing byλGr.Spacing adjustment of irregular eutectics occurs through the branching of the faceted phase.putational modeling of cast iron—analytical heat transport+transformation kineticsThe era of computational modeling of cast iron was started by the brilliancy of a scientist whose name has already been quoted several times in this paper.It is that of Oldfield[53],who,in 1966developed a computer model that could calculate the cool-ing curves of LG iron(Fig.12).His seminal paper included many innovations including parabolic laws with experimentally derived constants for nucleation and growth of spherical eutec-tic grains,correction for grain impingement against one another and against the wall,and a computer model for heatflow across a cylinder similar to FDM.Validation against published experi-ments was also included.Oldfield’s model is indisputably the basis of the current advances in computational modeling of microstructural evolution during solidification.Nobody ever remembers number2in any human endeavor. Yet,the author of this paper will have to take credit for this position,since in1973he was thefirst one to continue Oldfield’s work[54].Using an analytical model for heat transport and time stepping procedure to generate cooling curves,Stefanescu and Trufinescu[55]studied the effects of inoculants on the cooling curves and the nucleation constants.A third paper followed in 1978when Aizawa[56]used Oldfield’s model to examinethe Fig.12.Experimental and calculated cooling curves,quenched iron sample and equations for nucleation and growth proposed by Oldfield[53].influence of nucleation and growth rate constants on the width of the mushy zone in LG and SG iron.The next significant development in thefield belongs to Fredriksson and Svensson[57,58]who combined an analytical model for heat transfer with parabolic growth law for LG and white iron,carbon diffusion throughγshell for SG iron,and a model for cylindrical shape CG.They were also thefirst to introduce the Johnson–Mehl approximation for spherical grain impingement.At the same time and using similar procedures,Stefanescu and Kanetkar[59]included in the model primary and eutectic solidification,as well as the eutectoid transformation,calcu-lating for thefirst time the room temperature microstructure (Fig.13).Incremental improvements were contributed by various caze et al.[60]modified the mass balance equa-tion in the carbon diffusion model for SG iron to include calcula-tion of the off-eutectic austenite.Fras et al.[61]further improved the carbon diffusion model by solving for non-stationarydiffu-Fig.13.Calculated cooling curves(left)and fraction of phases(right).M is the cylindrical bar modulus.Full lines are for pearlite,dotted lines are for ferrite[59].330 D.M.Stefanescu /Materials Science and Engineering A 413–414(2005)322–333sion,including diffusion in liquid,and considering the ternary Fe C Si system.The next challenge of significant industrial interest was the prediction of the GWT.Fredriksson et al.[62]and Stefanescu and Kanetkar [63]approached it in 1986.By including both the stable and metastable phases in the calculation of the fraction solid,it was possible to output the solid fractions of gray and white eutectics.The basic equation was:f S =1−exp −4π3 N Gr r 3Gr +N Fe 3C r 3Fe3C where N is the number of grains and r is their putational modeling of cast iron—numericaltransport +transformation kineticsThe first coupled FDM energy transport–solidification kinet-ics model for SG iron was proposed in 1985by Su et al.[64].They used Oldfield’s nucleation model,carbon diffusion con-trolled growth through the γshell,and performed some valida-tion against experiment.It was not until 1991that a FDM energy transport–solidification kinetics model for SG iron was extended to room temperature by Chang et al.[65].They modeled the γ⇒αtransformation as a continuous cooling transformation and attempted some validation against experimental work.The first attempt to use a numerical model to predict the GWT appears to belong to Stefanescu and Kanetkar [66]who in 1987developed an axisymmetric implicit FDM heat transport model coupled with the description of the solidification kinetics of the stable and metastable eutectics.They validated model predictions against cast pin tests.A few years later,Nastac and Stefanescu [67]produced a complete FDM model for the prediction of the GWT,which was incorporated in ProCast.The model included the nucleation and growth of the stable and metastable phases and accounted for microsegregation.The model demonstrated such phenomena as the influence of Si segregation on the T st −T met interval for gray and white irons,and the influence of cooling rate and amount of Si on the gray-to-white and white-to-gray transitions (Fig.14).Mampey [68]included fluid flow in the transport calculations,compared filling simulation with experiment,and demonstrated the influence of mold filling on the final distribution of nod-ule count.He also illustrated the shifting of thermal center and the reduction of radial temperature differences when flow was included (Fig.15).putational modeling of cast iron—visualization of microstructureThe transformation of the computer into a dynamic micro-scope that transformed cast iron into a virtual material was spearheaded by Rappaz and his collaborators with their applica-tion of the cellular automaton (CA)technique to microstructure evolution modeling.Not surprisingly,the first application of the CA technique to cast iron is due to Charbon and Rappaz [69]who used the classic model for diffusion-controlled graphite growth through the austenite shell to describe SG ironsolidification.Fig.14.The influence of Si and initial cooling rate on structural transition in a 3.6%C,0.5%Mn,0.05%P,0.025%S cast iron [67].Two selected computer generated pictures at some intermedi-ate f S and at f S =1are presented in Fig.16.The reader will notice that each nodule is surrounded by an austenite grain.Yet experimental evidence suggests that more than one graphite spheroid is found in the eutectic austenite grains (see for exam-ple microshrinkage SEM images in Ref.[28]or color etching microstructures in Refs.[28,70]).Beltran-Sanchez and Stefanescu [71]improved on the previ-ous model by including solidification of primary austenite grains and by initiating graphite growth once graphite nuclei came in contact with the austenite grains.After contact,graphite was allowed to grow through the diffusion-controlled growth mech-anism.Fig.15.Calculated effect of fluid flow on the thermal profile of a cylindrical casting [68].。
Flow 研究概述
以上是 Flow 结构的一般理论模型，目前随着对 Flow 研究的进一步深入，某些应用领域也出现了一 些新的结构模型，如人机交互中的 Flow 因果结构模 型等（Novak, 1997）。
3 Flow 的特征和产生条件
研究表明，Flow 心理体验的产生具有跨阶层 性、跨性别性、跨年龄性、跨活动性和一定的跨文 化性（Sedig，2007）。处于 Flow 状态中的个体几乎 都有以下几个共同特征：（1）体验活动本身成为活 动的内在动机；（2）个体的注意力高度集中于当前 所从事的活动，任何其他的外在引诱最多也只可能 使个体出现暂时的分心；（3）自我意识的暂时丧失， 如忘记了自己的社会身份、忘记了自己的身体状况 （饥饿、疲劳）等；（4）行动与意识相融合；（5） 出现暂时性体验失真，较典型的如觉得时间过得比 平常要快；（6）对当前的活动具有较好的控制感， 即一个人能大致认识到自己能应对即将出现的后续 行为并能对它做出适当的反应；（7）具有直接的即 时反馈，活动的每一个环节都是对上一活动环节的 反馈；（8）个体所感知到的活动的挑战性和自身的 技能水平间具有平衡性；（9）有明确的活动目标 （Csikszentmihalyi，Abuhamdeh, & Jeanne, 2005）。
高
挑
战
觉醒
焦虑
Flow
担忧
控制
冷漠
放松
低
厌倦
挑
战
高技能
低技能
图 3 Flow 八通道模型（Csikszentmihalyi，Abuhamdeh, & Jeanne, 2005）
2.3 八通道模型 为了进一步增加自己理论的科学性，
Csikszentimihalyi 和他的研究小组在 1997 年又进一 步把四通道模型中的四种心理状态细分为八种不同 的心理状态（图 3），并用同心圆对他们各自的程度 进行了区分。这一模型在保留了技能水平和挑战水 平相适配这一中心观点之外，又确定了四个额外通 道：觉醒、控制、放松和担忧（Nakamura，2002）。 按照 Csikszentimihalyi 研究小组的最新观点，当外 在挑战过高时，它有可能不会对个体造成焦虑，个 体反而会出现一种无所谓的觉醒状态；同样当外在 挑战只是稍大于个体的能力时，个体也可能不会产 生焦虑体验，只是出现担忧等心理体验；当个体的 能力远远高于他所面临的挑战时，个体能毫不费力 地应对挑战，就可能不会产生厌烦体验，而是产生 轻松感和控制感等心理体验。因此，八通道模型在 某种程度上比前面的两种模型更科学，也更符合人 的实际状况。
1997年敖追求逃税游戏后期Cretaceous翻译
追求逃税游戏晚白垩世213追求逃税游戏在晚白垩世爱德华汉密尔顿肖恩答Menninga大卫塘卡尔文学院大瀑布城，美国密歇根49546（ehamil28，smenni23，dtong23）@ 顾问：加里W. Talsma摘要利用技术从微分对策理论，我们模型中的伶狩猎问题算法是指一个半独立的计算机。
通过定义行为在以下方面的捕食和简单，直观的首席，ciples，我们确定，设置另一个战略，以对付这样一个没有一个纯策略或捕食猎物的战略定义了一个最佳behav -IOR的格局。
相反，理想的策略或更纯净两间切换战略，在本质上不可预知，或千变万化，方式。
由此产生的最佳的行为表现出的假动作夫斯混合物，并为真正变成thescelosaurs，以及追求混合拦截和简单的预测在伶。
最后，利用这些策略，我们展示了一个决定性的优势伶伶狩猎狩猎在对超过孤立。
介绍我们描述一个狩猎的战略策略，它和飞行伶猎物用一个计算半离散差分代表性游戏追求和逃避。
首先，我们回顾形式主义的理论传统非微分对策和微分系统的分析扩展到了它的原则，仔细考虑伶问题方面的独特的说明。
该UMAP学报 18（3）（1997）213-224。
ç版权所有请注意：COMAP 1997年，公司保留所有权利。
审批使个人或课堂上使用数字或难以份工作的一部分或全部本被授予无偿提供的副本，又或为牟利或商业发行优势，承担本通知的副本。
信贷提取的，是允许的，但版权请注意：COMAP成分为别人所拥有的比这项工作必须得到遵守。
要复制，否则，重新发布，发布服务器上，或重新分配到清单需要从COMAP是事先许可。
爱德华汉密尔顿，肖恩答Menninga，大卫塘美国卡尔文学院（卡尔文学院）1997年的角逐中脱颖而出数学中国提供：214该UMAP杂志18.3第二，我们提出了一个假设最小设置所需，以减少分析数字游戏，一时间迭代计算机算法，并提交一小部分执行每一个参与者直观简单策略。
第三，我们研究完善，影响了双方的假设不完全信息的战略优化为一个单一的追求者和单回避者，然后扩展到更多的追兵这些结论。
运动治疗指南
Exercise and Type2DiabetesThe American College of Sports Medicine and the American Diabetes Association:joint position statementS HERI R.C OLBERG,PHD,FACSM1R ONALD J.S IGAL,MD,MPH,FRCP(C)2 B O F ERNHALL,PHD,FACSM3J UDITH G.R EGENSTEINER,PHD4B RYAN J.B LISSMER,PHD5R ICHARD R.R UBIN,PHD6L ISA C HASAN-T ABER,SCD,FACSM7A NN L.A LBRIGHT,PHD,RD8B ARRY B RAUN,PHD,FACSM9Although physical activity(PA)is a key element in the prevention and management of type2 diabetes,many with this chronic disease do not become or remain regularly active.High-quality studies establishing the importance of exercise andfitness in diabetes were lacking until recently, but it is now well established that participation in regular PA improves blood glucose control and can prevent or delay type2diabetes,along with positively affecting lipids,blood pressure, cardiovascular events,mortality,and quality of life.Structured interventions combining PA and modest weight loss have been shown to lower type2diabetes risk by up to58%in high-risk populations.Most benefits of PA on diabetes management are realized through acute and chronic improvements in insulin action,accomplished with both aerobic and resistance training.The benefits of physical training are discussed,along with recommendations for varying activities, PA-associated blood glucose management,diabetes prevention,gestational diabetes mellitus, and safe and effective practices for PA with diabetes-related complications.Diabetes Care33:e147–e167,2010 INTRODUCTIOND iabetes has become a widespreadepidemic,primarily because of theincreasing prevalence and inci-dence of type2diabetes.According to the Centers for Disease Control and Preven-tion,in2007,almost24million Ameri-cans had diabetes,with one-quarter of those,or six million,undiagnosed(261). Currently,it is estimated that almost60 million U.S.residents also have prediabe-tes,a condition in which blood glucose(BG)levels are above normal,thus greatlyincreasing their risk for type2diabetes(261).Lifetime risk estimates suggest thatone in three Americans born in2000orlater will develop diabetes,but in high-risk ethnic populations,closer to50%may develop it(200).Type2diabetes is asignificant cause of premature mortalityand morbidity related to cardiovasculardisease(CVD),blindness,kidney andnerve disease,and amputation(261).Al-though regular physical activity(PA)mayprevent or delay diabetes and its compli-cations(10,46,89,112,176,208,259,294),most people with type2diabetes are notactive(193).In this article,the broader term“physical activity”(defined as“bodilymovement produced by the contractionof skeletal muscle that substantially in-creases energy expenditure”)is used in-terchangeably with“exercise,”which isdefined as“a subset of PA done with theintention of developing physicalfitness(i.e.,cardiovascular[CV],strength,andflexibility training).”The intent is to rec-ognize that many types of physical move-ment may have a positive effect onphysicalfitness,morbidity,and mortalityin individuals with type2diabetes.Diagnosis,classification,andetiology of diabetesCurrently,the American Diabetes Associ-ation(ADA)recommends the use of anyof the following four criteria for diagnos-ing diabetes:1)glycated hemoglobin(A1C)value of6.5%or higher,2)fastingplasma glucoseՆ126mg/dl(7.0mmol/l),3)2-h plasma glucoseՆ200mg/dl(11.1mmol/l)during an oral glucose tol-erance test using75g of glucose,and/or4)classic symptoms of hyperglycemia(e.g.,polyuria,polydipsia,and unex-plained weight loss)or hyperglycemic cri-sis with a random plasma glucose of200mg/dl(11.1mmol/l)or higher.In the ab-sence of unequivocal hyperglycemia,thefirst three criteria should be confirmed byrepeat testing(4).Prediabetes is diag-nosed with an A1C of5.7–6.4%,fastingplasma glucose of100–125mg/dl(5.6–6.9mmol/l;i.e.,impaired fasting glucose[IFG]),or2-h postload glucose of140–199mg/dl(7.8–11.0mmol/l;i.e.,im-paired glucose tolerance[IGT])(4).The major forms of diabetes can becategorized as type1or type2(4).In type1diabetes,which accounts for5–10%ofcases,the cause is an absolute deficiencyof insulin secretion resulting from auto-immune destruction of the insulin-producing cells in the pancreas.Type2●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●From the1Human Movement Sciences Department,Old Dominion University,Norfolk,Virginia;the2De-partments of Medicine,Cardiac Sciences,and Community Health Sciences,Faculties of Medicine and Kinesiology,University of Calgary,Calgary,Alberta,Canada;the3Department of Kinesiology and Com-munity Health,University of Illinois at Urbana-Champaign,Urbana,Illinois;the4Divisions of General Internal Medicine and Cardiology and Center for Women’s Health Research,University of Colorado School of Medicine,Aurora,Colorado;the5Department of Kinesiology and Cancer Prevention Research Center,University of Rhode Island,Kingston,Rhode Island;the6Departments of Medicine and Pediatrics, The Johns Hopkins University School of Medicine,Baltimore,Maryland;the7Division of Biostatistics and Epidemiology,University of Massachusetts,Amherst,Massachusetts;the8Division of Diabetes Transla-tion,Centers for Disease Control and Prevention,Atlanta,Georgia;and the9Department of Kinesiology, University of Massachusetts,Amherst,Massachusetts.Corresponding author:Sheri R.Colberg,scolberg@.This joint position statement is written by the American College of Sports Medicine and the American Diabetes Association and was approved by the Executive Committee of the American Diabetes Association in July2010.This statement is published concurrently in Medicine&Science in Sports&Exercise and Diabetes Care.Individual name recognition is stated in the ACKNOWLEDGMENTS at the end of the statement.Thefindings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.DOI:10.2337/dc10-9990©2010by the American Diabetes Association.Readers may use this article as long as the work is properly cited,the use is educational and not for profit,and the work is not altered.See http://creativecommons.org/licenses/by-nc-nd/3.0/for details.See accompanying article,p.2692.R e v i e w s/C o m m e n t a r i e s/A D A S t a t e m e n t sdiabetes(90–95%of cases)results from a combination of the inability of muscle cells to respond to insulin properly(insu-lin resistance)and inadequate compensa-tory insulin secretion.Less common forms include gestational diabetes melli-tus(GDM),which is associated with a 40–60%chance of developing type2di-abetes in the next5–10years(261).Dia-betes can also result from genetic defects in insulin action,pancreatic disease,sur-gery,infections,and drugs or chemicals (4,261).Genetic and environmental factors are strongly implicated in the develop-ment of type2diabetes.The exact genetic defects are complex and not clearly de-fined(4),but risk increases with age,obe-sity,and physical inactivity.Type2 diabetes occurs more frequently in popu-lations with hypertension or dyslipide-mia,women with previous GDM,and non-Caucasian people including Native Americans,African Americans,Hispanic/ Latinos,Asians,and Pacific Islanders. Treatment goals in type2diabetes The goal of treatment in type2diabetes is to achieve and maintain optimal BG, lipid,and blood pressure(BP)levels to prevent or delay chronic complications of diabetes(5).Many people with type2di-abetes can achieve BG control by follow-ing a nutritious meal plan and exercise program,losing excess weight,imple-menting necessary self-care behaviors, and taking oral medications,although others may need supplemental insulin (261).Diet and PA are central to the man-agement and prevention of type2diabe-tes because they help treat the associated glucose,lipid,BP control abnormalities, as well as aid in weight loss and mainte-nance.When medications are used to control type2diabetes,they should aug-ment lifestyle improvements,not replace them.ACUTE EFFECTS OFEXERCISEFuel metabolism during exercise Fuel mobilization,glucose production, and muscle glycogenolysis.The main-tenance of normal BG at rest and during exercise depends largely on the coordina-tion and integration of the sympathetic nervous and endocrine systems(250). Contracting muscles increase uptake of BG,although BG levels are usually main-tained by glucose production via liver gly-cogenolysis and gluconeogenesis and mobilization of alternate fuels,such asfree fatty acids(FFAs)(250,268).Several factors influence exercise fueluse,but the most important are the inten-sity and duration of PA(9,29,47,83,111,133,160,181,241).Any activity causes ashift from predominant reliance on FFA atrest to a blend of fat,glucose,and muscleglycogen,with a small contributionfrom amino acids(15,31).With in-creasing exercise intensity,there is agreater reliance on carbohydrate as longas sufficient amounts are available inmuscle or blood(21,23,47,133).Earlyin exercise,glycogen provides the bulkof the fuel for working muscles.As gly-cogen stores become depleted,musclesincrease their uptake and use of circu-lating BG,along with FFA released fromadipose tissue(15,132,271).Intramus-cular lipid stores are more readily usedduring longer-duration activities andrecovery(23,223,270).Glucose pro-duction also shifts from hepatic glyco-genolysis to enhanced gluconeogenesisas duration increases(250,268).Evidence statement.PA causes increasedglucose uptake into active muscles bal-anced by hepatic glucose production,with a greater reliance on carbohydrate tofuel muscular activity as intensity in-creases.The American College of SportsMedicine(ACSM)evidence category A(seeTables1and2for explanation).Insulin-independent and insulin-dependent muscle glucose uptake dur-ing exercise.There are two well-definedpathways that stimulate glucose uptakeby muscle(96).At rest and postprandi-ally,its uptake by muscle is insulin de-pendent and serves primarily to replenishmuscle glycogen stores.During exercise,contractions increase BG uptake to sup-plement intramuscular glycogenolysis(220,227).As the two pathways are dis-tinct,BG uptake into working muscle isnormal even when insulin-mediated up-take is impaired in type2diabetes(28,47,293).Muscular BG uptake re-mains elevated postexercise,with thecontraction-mediated pathway persist-ing for several hours(86,119)and insulin-mediated uptake for longer(9,33,141,226).Glucose transport into skeletal mus-cle is accomplished via GLUT proteins,with GLUT4being the main isoform inmuscle modulated by both insulin andcontractions(110,138).Insulin activatesGLUT4translocation through a complexsignaling cascade(256,293).Contrac-tions,however,trigger GLUT4transloca-tion at least in part through activation of5Ј-AMP–activated protein kinase(198,293).Insulin-stimulated GLUT4translocation is generally impaired in type2diabetes(96).Both aerobic and resis-tance exercises increase GLUT4abun-dance and BG uptake,even in the presenceof type2diabetes(39,51,204,270).Evidence statement.Insulin-stimulatedBG uptake into skeletal muscle predomi-nates at rest and is impaired in type2diabetes,while muscular contractionsstimulate BG transport via a separate ad-ditive mechanism not impaired by insulinresistance or type2diabetes.ACSM evi-dence category A.Postexercise glycemic control/BGlevelsAerobic exercise effects.During mod-erate-intensity exercise in nondiabeticpersons,the rise in peripheral glucose up-take is matched by an equal rise in hepaticglucose production,the result being thatBG does not change except during pro-longed,glycogen-depleting exercise.Inindividuals with type2diabetes perform-ing moderate exercise,BG utilization bymuscles usually rises more than hepaticglucose production,and BG levels tend todecline(191).Plasma insulin levels nor-mally fall,however,making the risk ofexercise-induced hypoglycemia in any-one not taking insulin or insulin secreta-gogues very minimal,even withprolonged PA(152).The effects of a sin-gle bout of aerobic exercise on insulin ac-tion vary with duration,intensity,andsubsequent diet;a single session in-creases insulin action and glucose toler-ance for more than24h but less than72h(26,33,85,141).The effects ofmoderate aerobic exercise are similarwhether the PA is performed in a singlesession or multiple bouts with the sametotal duration(14).During brief,intense aerobic exercise,plasma catecholamine levels rise mark-edly,driving a major increase in glucoseproduction(184).Hyperglycemia can re-sult from such activity and persist for upto1–2h,likely because plasma catechol-amine levels and glucose production donot return to normal immediately withcessation of the activity(184).Evidence statement.Although moderateaerobic exercise improves BG and insulin ac-tion acutely,the risk of exercise-induced hy-poglycemia is minimal without use ofexogenous insulin or insulin secretagogues.Transient hyperglycemia can follow intensePA.ACSM evidence category C.Exercise and type2diabetesResistance exercise effects.The acute effects of a single bout of resistance train-ing on BG levels and/or insulin action in individuals with type2diabetes have not been reported.In individuals with IFG (BG levels of100–125mg/dl),resistance exercise results in lower fasting BG levels 24h after exercise,with greater reduc-tions in response to both volume(multi-ple-vs.single-set sessions)and intensity of resistance exercise(vigorous compared with moderate)(18).Evidence statement.The acute effects of resistance exercise in type2diabetes have not been reported,but result in lower fast-ing BG levels for at least24h after exercise in individuals with IFG.ACSM evidence category C.Combined aerobic and resistance and other types of training.A combination of aerobic and resistance training may be more effective for BG management than either type of exercise alone(51,238).Any increase in muscle mass that may re-sult from resistance training could con-tribute to BG uptake without altering themuscle’s intrinsic capacity to respond toinsulin,whereas aerobic exercise en-hances its uptake via a greater insulin ac-tion,independent of changes in musclemass or aerobic capacity(51).However,all reported combination training had agreater total duration of exercise and ca-loric use than when each type of trainingwas undertaken alone(51,183,238).Mild-intensity exercises such as tai chiand yoga have also been investigated fortheir potential to improve BG manage-ment,with mixed results(98,117,159,257,269,286,291).Although tai chi maylead to short-term improvements in BGlevels,effects from long-term training(i.e.,16weeks)do not seem to last72hafter the last session(257).Some studieshave shown lower overall BG levels withextended participation in such activities(286,291),although others have not(159,257).One study suggested that yo-ga’s benefits on fasting BG,lipids,oxida-tive stress markers,and antioxidant statusare at least equivalent to more conven-tional forms of PA(98).However,a meta-analysis of yoga studies stated that thelimitations characterizing most studies,such as small sample size and varyingforms of yoga,preclude drawingfirmconclusions about benefits to diabetesmanagement(117).Evidence statement.A combination ofaerobic and resistance exercise trainingmay be more effective in improving BGcontrol than either alone;however,morestudies are needed to determine if totalcaloric expenditure,exercise duration,orexercise mode is responsible.ACSM evi-dence category der forms of exercise(e.g.,tai chi,yoga)have shown mixed re-sults.ACSM evidence category C.Table1—Evidence categories for ACSM and evidence-grading system for clinical practice recommendations for ADAI.ACSM evidence categoriesEvidencecategory Source of evidence DefinitionA Randomized,controlled trials(overwhelming data)Provides a consistent pattern offindings with substantial studiesB Randomized,controlled trials(limited data)Few randomized trials exist,which are small in size,and results are inconsistentC Nonrandomized trials,observational studies Outcomes are from uncontrolled,nonrandomized,and/or observational studiesD Panel consensus judgment Panel’s expert opinion when the evidence is insufficient to place it in categoriesA–CII.ADA evidence-grading system for clinical practice recommendationsLevel ofevidence DescriptionA Clear evidence from well-conducted,generalizable,randomized,controlled trials that are adequately powered,including thefollowing:•Evidence from a well-conducted multicenter trial•Evidence from a meta-analysis that incorporated quality ratings in the analysisCompelling nonexperimental evidence,i.e.,the“all-or-none”rule developed by the Centre for Evidence-Based Medicine at OxfordSupportive evidence from well-conducted,randomized,controlled trials that are adequately powered,including the following:•Evidence from a well-conducted trial at one or more institutions•Evidence from a meta-analysis that incorporated quality ratings in the analysisB Supportive evidence from well-conducted cohort studies,including the following:•Evidence from a well-conducted prospective cohort study or registry•Evidence from a well-conducted meta-analysis of cohort studiesSupportive evidence from a well-conducted case-control studyC Supportive evidence from poorly controlled or uncontrolled studies,including the following:•Evidence from randomized clinical trials with one or more major or three or more minor methodologicalflaws that couldinvalidate the results•Evidence from observational studies with high potential for bias(such as case series with comparison to historical controls)•Evidence from case series or case reportsConflicting evidence with the weight of evidence supporting the recommendationE Expert consensus or clinical experienceColberg and AssociatesTable2—Summary of ACSM evidence and ADA clinical practice recommendation statementsACSM evidence and ADA clinical practice recommendation statements ACSM evidence category (A,highest;D,lowest)/ ADA level of evidence (A,highest;E,lowest)Acute effects of exercise•PA causes increased glucose uptake into active muscles balanced by hepatic glucoseproduction,with a greater reliance on carbohydrate to fuel muscular activity as intensityincreases.A/*•Insulin-stimulated BG uptake into skeletal muscle predominates at rest and is impairedin type2diabetes,while muscular contractions stimulate BG transport via a separate,additive mechanism not impaired by insulin resistance or type2diabetes.A/*•Although moderate aerobic exercise improves BG and insulin action acutely,the risk ofexercise-induced hypoglycemia is minimal without use of exogenous insulin or insulinsecretagogues.Transient hyperglycemia can follow intense PA.C/*•The acute effects of resistance exercise in type2diabetes have not been reported,butresult in lower fasting BG levels for at least24h postexercise in individuals with IFG.C/*•A combination of aerobic and resistance exercise training may be more effective inimproving BG control than either alone;however,more studies are needed todetermine whether total caloric expenditure,exercise duration,or exercise mode isresponsible.B/*•Milder forms of exercise(e.g.,tai chi,yoga)have shown mixed results.C/*•PA can result in acute improvements in systemic insulin action lasting from2to72h.A/*Chronic effects of exercise training •Both aerobic and resistance training improve insulin action,BG control,and fatoxidation and storage in muscle.B/*•Resistance exercise enhances skeletal muscle mass.A/*•Blood lipid responses to training are mixed but may result in a small reduction in LDLcholesterol with no change in HDL cholesterol or bined weight lossand PA may be more effective than aerobic exercise training alone on lipids.C/*•Aerobic training may slightly reduce systolic BP,but reductions in diastolic BP are lesscommon,in individuals with type2diabetes.C/*•Observational studies suggest that greater PA andfitness are associated with a lowerrisk of all-cause and CV mortality.C/*•Recommended levels of PA may help produce weight loss.However,up to60min/daymay be required when relying on exercise alone for weight loss.C/*•Individuals with type2diabetes engaged in supervised training exhibit greatercompliance and BG control than those undertaking exercise training withoutsupervision.B/*•Increased PA and physicalfitness can reduce symptoms of depression and improvehealth-related QOL in those with type2diabetes.B/*PA and prevention of type2diabetes •At least2.5h/week of moderate to vigorous PA should be undertaken as part oflifestyle changes to prevent type2diabetes onset in high-risk adults.A/APA in prevention and control of GDM •Epidemiological studies suggest that higher levels of PA may reduce risk of developingGDM during pregnancy.C/*•RCTs suggest that moderate exercise may lower maternal BG levels in GDM.B/*Preexercise evaluation•Before undertaking exercise more intense than brisk walking,sedentary persons withtype2diabetes will likely benefit from an evaluation by a physician.ECG exercisestress testing for asymptomatic individuals at low risk of CAD is not recommended butmay be indicated for higher risk.C/CRecommended PA participation for persons with type2 diabetes •Persons with type2diabetes should undertake at least150min/week of moderate tovigorous aerobic exercise spread out during at least3days during the week,with nomore than2consecutive days between bouts of aerobic activity.B/B•In addition to aerobic training,persons with type2diabetes should undertakemoderate to vigorous resistance training at least2–3days/week.B/B •Supervised and combined aerobic and resistance training may confer additional healthbenefits,although milder forms of PA(such as yoga)have shown mixed results.Persons with type2diabetes are encouraged to increase their total daily unstructuredPA.Flexibility training may be included but should not be undertaken in place ofother recommended types of PA.B/C(continued)Exercise and type2diabetesInsulin resistanceAcute changes in muscular insulin re-sistance.Most benefits of PA on type2 diabetes management and prevention are realized through acute and chronic im-provements in insulin action(29,46, 116,118,282).The acute effects of a re-cent bout of exercise account for most of the improvements in insulin action,with most individuals experiencing a decrease in their BG levels during mild-and mod-erate-intensity exercise and for2–72h af-terward(24,83,204).BG reductions are related to the dura-tion and intensity of the exercise,preex-ercise control,and state of physical training(24,26,47,238).Although previ-ous PA of any intensity generally exerts its effects by enhancing uptake of BG for gly-cogen synthesis(40,83)and by stimulat-ing fat oxidation and storage in muscle (21,64,95),more prolonged or intense PAacutely enhances insulin action for longerperiods(9,29,75,111,160,238).Acute improvements in insulin sensi-tivity in women with type2diabetes havebeen found for equivalent energy expen-ditures whether engaging in low-intensityor high-intensity walking(29)but may beaffected by age and training status(24,75,100,101,228).For example,mod-erate-to heavy-intensity aerobic trainingundertaken three times a week for6months improved insulin action in bothyounger and older women but persistedonly in the younger group for72–120h.Acute changes in liver’s ability to pro-cess glucose.Increases in liver fat con-tent common in obesity and type2diabetesare strongly associated with reduced he-patic and peripheral insulin action.En-hanced whole-body insulin action afteraerobic training seems to be related to gainsin peripheral,not hepatic,insulin action(146,282).Such training not resulting inoverall weight loss may still reduce hepaticlipid content and alter fat partitioning anduse in the liver(128).Evidence statement.PA can result inacute improvements in systemic insulinaction lasting from2to72h.ACSM evi-dence category A.CHRONIC EFFECTS OFEXERCISE TRAININGMetabolic control:BG levels and insu-lin resistance.Aerobic exercise has beenthe mode traditionally prescribed for dia-betes prevention and management.Even1week of aerobic training can improvewhole-body insulin sensitivity in individ-uals with type2diabetes(282).Moderateand vigorous aerobic training improve in-Table2—ContinuedACSM evidence and ADA clinical practice recommendation statements ACSM evidence category (A,highest;D,lowest)/ ADA level of evidence (A,highest;E,lowest)Exercise with nonoptimal BG control •Individuals with type2diabetes may engage in PA,using caution when exercising withBG levels exceeding300mg/dl(16.7mmol/l)without ketosis,provided they arefeeling well and are adequately hydrated.C/E•Persons with type2diabetes not using insulin or insulin secretagogues are unlikely toexperience hypoglycemia related to ers of insulin and insulin secretagogues areadvised to supplement with carbohydrate as needed to prevent hypoglycemia duringand after exercise.C/CMedication effects on exercise responses •Medication dosage adjustments to prevent exercise-associated hypoglycemia may berequired by individuals using insulin or certain insulin secretagogues.Most othermedications prescribed for concomitant health problems do not affect exercise,withthe exception of-blockers,some diuretics,and statins.C/CExercise with long-term complications of diabetes •Known CVD is not an absolute contraindication to exercise.Individuals with anginaclassified as moderate or high risk should likely begin exercise in a supervised cardiacrehabilitation program.PA is advised for anyone with PAD.C/C•Individuals with peripheral neuropathy and without acute ulceration may participatein moderate weight-bearing prehensive foot care including dailyinspection of feet and use of proper footwear is recommended for prevention and earlydetection of sores or ulcers.Moderate walking likely does not increase risk of footulcers or reulceration with peripheral neuropathy.B/B•Individuals with CAN should be screened and receive physician approval and possiblyan exercise stress test before exercise initiation.Exercise intensity is best prescribedusing the HR reserve method with direct measurement of maximal HR.C/C•Individuals with uncontrolled proliferative retinopathy should avoid activities thatgreatly increase intraocular pressure and hemorrhage risk.D/E•Exercise training increases physical function and QOL in individuals with kidneydisease and may even be undertaken during dialysis sessions.The presence ofmicroalbuminuria per se does not necessitate exercise restrictions.C/CAdoption and maintenance of exercise by persons with diabetes •Efforts to promote PA should focus on developing self-efficacy and fostering socialsupport from family,friends,and health care providers.Encouraging mild or moderatePA may be most beneficial to adoption and maintenance of regular PA participation.Lifestyle interventions may have some efficacy in promoting PA behavior.B/B*No recommendation given.Colberg and Associates。